Practice of epidemiology: Hierarchical latency models for dose-time-response associations

David B. Richardson, Richard F. MacLehose, Bryan Langholz, Stephen R. Cole

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Exposure lagging and exposure-time window analysis are 2 widely used approaches to allow for induction and latency periods in analyses of exposure-disease associations. Exposure lagging implies a strong parametric assumption about the temporal evolution of the exposure-disease association. An exposure-time window analysis allows for a more flexible description of temporal variation in exposure effects but may result in unstable risk estimates that are sensitive to how windows are defined. The authors describe a hierarchical regression approach that combines time window analysis with a parametric latency model. They illustrate this approach using data from 2 occupational cohort studies: studies of lung cancer mortality among 1) asbestos textile workers and 2) uranium miners. For each cohort, an exposure-time window analysis was compared with a hierarchical regression analysis with shrinkage toward a simpler, second-stage parametric latency model. In each cohort analysis, there is substantial stability gained in time window-specific estimates of association by using a hierarchical regression approach. The proposed hierarchical regression model couples a time window analysis with a parametric latency model; this approach provides a way to stabilize risk estimates derived from a time window analysis and a way to reduce bias arising from misspecification of a parametric latency model.

Original languageEnglish (US)
Pages (from-to)695-702
Number of pages8
JournalAmerican journal of epidemiology
Volume173
Issue number6
DOIs
StatePublished - Mar 15 2011

Keywords

  • Cohort studies
  • Hierarchical model
  • Latency
  • Neoplasms
  • Regression

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